The need for detecting and tracking important objects in near real time are presented in many military and civilian domains. A military case would be tracking a high value object or a person of a target, either ally or enemy. A civilian and national priority example might be child protection and tracking. Keeping track of a child or a mobile object is often a difficult task for a parent or a single guardian.
If it were possible to exploit a community and pervasive smartphones for object or child tracking, for example by a tracking system in which a monitored object(s) or person/child emit a wireless signal via a small tag, where that wireless signal could be captured by a mobile communications device, there would exist several technical issues hindering its practical deployment such as privacy and security, and energy efficiency.
The sensing paradigm of using mobile devices carried by humans for a collaborative objective is called Mobile Crowd Sensing (MCS) [1] or People-Centric Sensing [2], and received recent attention from academia [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. However, while several efforts have been made to improve specific techniques for MCS, ranging from incentive schemes [15], a programming framework for MCS applications [4], and a deployment model [7] to energy efficiency [6], thus far, there have been few actual deployments of MCS for real-world public sector applications of a national priority. To become a really effective technology, MCS still has to overcome the major challenges of privacy, security, sensing effectiveness, and user resource utilization. To the best of our knowledge, the particular privacy issue of MCS has not been technically addressed before. There are a number of commercial devices available for child tracking [16, 17, 18, 19]. However, most of them are expensive and fundamentally rely on GPS that is not available in-doors nor is it energy-efficient. Furthermore, these proposed methods use cellular communication for one-to-one communication rather than a crowd sensing application. Thus these methods incur monthly-charges and the efficacy of that form of monitoring is limited compared to crowd sensing.